A cross-channel GAN for video anomaly detection improves UCSD Ped2 AUC from 93.7% to 98.0% by adding a cycle-consistency loss and morphological noise suppression.
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What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks
A cross-channel GAN for video anomaly detection improves UCSD Ped2 AUC from 93.7% to 98.0% by adding a cycle-consistency loss and morphological noise suppression.